"""simple docstring"""


import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


a				=trt.Logger(trt.Logger.WARNING)
a				=absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

a				=logging.getLogger(__name__)

a				=argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=384,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=128,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=20,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=30,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

a				=parser.parse_args()

if args.tokenizer_name:
	a				=AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
	raise ValueError(
	    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
	    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
	)

logger.info('Training/evaluation parameters %s', args)

a				=args.per_device_eval_batch_size

a				=(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a				=True

a				='temp_engine/bert-fp32.engine'
if args.fpaa:
	a				='temp_engine/bert-fp16.engine'
if args.inta:
	a				='temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists('temp_engine'):
	os.makedirs('temp_engine')

a				=1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
	with open(args.onnx_model_path, 'rb') as model:
		if not parser.parse(model.read()):
			for error in range(parser.num_errors):
				print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
	a				=[network.get_input(i) for i in range(network.num_inputs)]
	a				=[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

	with builder.create_builder_config() as config:
		a				=1 << 50
		if STRICT_TYPES:
			config.set_flag(trt.BuilderFlag.STRICT_TYPES)
		if args.fpaa:
			config.set_flag(trt.BuilderFlag.FPaa)
		if args.inta:
			config.set_flag(trt.BuilderFlag.INTa)
		a				=builder.create_optimization_profile()
		config.add_optimization_profile(profile)
		for i in range(len(input_names)):
			profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
		a				=builder.build_engine(network, config)

		# serialize_engine and store in file (can be directly loaded and deserialized):
		with open(engine_name, 'wb') as f:
			f.write(engine.serialize())





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =np.asarray(inputs["input_ids"]  ,       dtype=np.intaa							)
						lowerCamelCase__   =np.asarray(inputs["attention_mask"]  ,       dtype=np.intaa							)
						lowerCamelCase__   =np.asarray(inputs["token_type_ids"]  ,       dtype=np.intaa							)

						# Copy inputs
						cuda.memcpy_htod_async(d_inputs[0]  ,       input_ids.ravel()  ,       __lowerCAmelCase							)
						cuda.memcpy_htod_async(d_inputs[1]  ,       attention_mask.ravel()  ,       __lowerCAmelCase							)
						cuda.memcpy_htod_async(d_inputs[2]  ,       token_type_ids.ravel()  ,       __lowerCAmelCase							)
						# start time
						lowerCamelCase__   =time.time()
						# Run inference
						context.execute_async(
						    bindings=[int(__lowerCAmelCase							) for d_inp in d_inputs] + [int(__lowerCAmelCase							), int(__lowerCAmelCase							)]  ,       stream_handle=stream.handle							)
						# Transfer predictions back from GPU
						cuda.memcpy_dtoh_async(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						cuda.memcpy_dtoh_async(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						# Synchronize the stream and take time
						stream.synchronize()
						# end time
						lowerCamelCase__   =time.time()
						lowerCamelCase__   =end_time - start_time
						lowerCamelCase__   =(h_outputa, h_outputa)
						# print(outputs)
						return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
a				=Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
	datasets.utils.logging.set_verbosity_warning()
	transformers.utils.logging.set_verbosity_info()
else:
	datasets.utils.logging.set_verbosity_error()
	transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
	set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
	# Downloading and loading a dataset from the hub.
	a				=load_dataset(args.dataset_name, args.dataset_config_name)
else:
	raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

a				=raw_datasets['validation'].column_names

a				='question' if 'question' in column_names else column_names[0]
a				='context' if 'context' in column_names else column_names[1]
a				='answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
a				=tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
	logger.warning(
	    F'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
	    F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
	)

a				=min(args.max_seq_length, tokenizer.model_max_length)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   =[q.lstrip() for q in examples[question_column_name]]

						# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
						# in one example possible giving several features when a context is long, each of those features having a
						# context that overlaps a bit the context of the previous feature.
						lowerCamelCase__   =tokenizer(
						    examples[question_column_name if pad_on_right else context_column_name]  ,       examples[context_column_name if pad_on_right else question_column_name]  ,       truncation="only_second" if pad_on_right else "only_first"  ,       max_length=__lowerCAmelCase  ,       stride=args.doc_stride  ,       return_overflowing_tokens=__lowerCAmelCase  ,       return_offsets_mapping=__lowerCAmelCase  ,       padding="max_length"  ,       )

						# Since one example might give us several features if it has a long context, we need a map from a feature to
						# its corresponding example. This key gives us just that.
						lowerCamelCase__   =tokenized_examples.pop("overflow_to_sample_mapping"							)

						# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
						# corresponding example_id and we will store the offset mappings.
						lowerCamelCase__   =[]

						for i in range(len(tokenized_examples["input_ids"]							)							):
												# Grab the sequence corresponding to that example (to know what is the context and what is the question).
												lowerCamelCase__   =tokenized_examples.sequence_ids(__lowerCAmelCase							)
												lowerCamelCase__   =1 if pad_on_right else 0

												# One example can give several spans, this is the index of the example containing this span of text.
												lowerCamelCase__   =sample_mapping[i]
												tokenized_examples["example_id"].append(examples["id"][sample_index]							)

												# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
												# position is part of the context or not.
												lowerCamelCase__   =[
												    (o if sequence_ids[k] == context_index else None)
												    for k, o in enumerate(tokenized_examples["offset_mapping"][i]							)
												]

						return tokenized_examples


a				=raw_datasets['validation']
# Validation Feature Creation
a				=eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='Running tokenizer on validation dataset',
)

a				=default_data_collator

a				=eval_dataset.remove_columns(['example_id', 'offset_mapping'])
a				=DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase="eval"							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =postprocess_qa_predictions(
						    examples=__lowerCAmelCase  ,       features=__lowerCAmelCase  ,       predictions=__lowerCAmelCase  ,       version_2_with_negative=args.version_2_with_negative  ,       n_best_size=args.n_best_size  ,       max_answer_length=args.max_answer_length  ,       null_score_diff_threshold=args.null_score_diff_threshold  ,       output_dir=args.output_dir  ,       prefix=__lowerCAmelCase  ,       )
						# Format the result to the format the metric expects.
						if args.version_2_with_negative:
												lowerCamelCase__   =[
												    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
												]
						else:
												lowerCamelCase__   =[{"id": k, "prediction_text": v} for k, v in predictions.items()]

						lowerCamelCase__   =[{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
						return EvalPrediction(predictions=__lowerCAmelCase  ,       label_ids=__lowerCAmelCase							)


a				=load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
	# setup for TRT inferrence
	for i in range(len(input_names)):
		context.set_binding_shape(i, INPUT_SHAPE)
	assert context.all_binding_shapes_specified





	def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




							'''simple docstring'''



							return trt.volume(engine.get_binding_shape(__lowerCAmelCase							)							) * engine.get_binding_dtype(__lowerCAmelCase							).itemsize

	# Allocate device memory for inputs and outputs.
	a				=[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

	# Allocate output buffer
	a				=cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
	a				=cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
	a				=cuda.mem_alloc(h_outputa.nbytes)
	a				=cuda.mem_alloc(h_outputa.nbytes)

	# Create a stream in which to copy inputs/outputs and run inference.
	a				=cuda.Stream()

	# Evaluation
	logger.info('***** Running Evaluation *****')
	logger.info(F'''  Num examples = {len(eval_dataset)}''')
	logger.info(F'''  Batch size = {args.per_device_eval_batch_size}''')

	a				=0.0
	a				=0
	a				=timeit.default_timer()

	a				=None
	for step, batch in enumerate(eval_dataloader):
		a				, a				=model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
		total_time += infer_time
		niter += 1

		a				, a				=outputs
		a				=torch.tensor(start_logits)
		a				=torch.tensor(end_logits)

		# necessary to pad predictions and labels for being gathered
		a				=accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
		a				=accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

		a				=(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
		a				=logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

	if all_preds is not None:
		a				=nested_truncate(all_preds, len(eval_dataset))

	a				=timeit.default_timer() - start_time
	logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
	# Inference time from TRT
	logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1000 / niter))
	logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))





	logger.info('Total Number of Inference =  %d', niter)

a				=post_processing_function(eval_examples, eval_dataset, all_preds)
a				=metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





logger.info(F'''Evaluation metrics: {eval_metric}''')



"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				={
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a				=[
	    'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MgpstrModel',
	    'MgpstrPreTrainedModel',
	    'MgpstrForSceneTextRecognition',
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	a				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


